Proceedings ArticleDOI
Use of leaf colour for drought stress analysis in rice
Swati Bhugra,Santanu Chaudhury,Brejesh Lall +2 more
- pp 1-4
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TLDR
The proposed technique uses leaf color as the phenomic trait to assess stress levels using Relative water content (RWC) as a quantitative proxy and extracted the change in leaf color in response to drought stress using the color features obtained using Random forest.Abstract:
We propose a novel approach of utilizing phenomic traits to automatically quantify stress in plants using machine learning techniques. Moisture deficit conditions cause change in leaf color due to decrease in chlorophyll content as chloroplast is damaged by active oxygen species. Therefore, the proposed technique uses leaf color as the phenomic trait to assess stress levels using Relative water content (RWC) as a quantitative proxy. We extracted the change in leaf color in response to drought stress using the color features obtained using Random forest. A regressor has been modeled to predict the stress level of rice genotypes via RWC by employing colour histogram as a feature vector. The experiment was performed with pot images of different rice genotypes under normal and drought stressed conditions. We report a correlation coefficient of 0.89 obtained using this model demonstrating the capability of the presented technique for stress level predictions.read more
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Journal ArticleDOI
Real-Time Plant Health Detection Using Deep Convolutional Neural Networks
Mahnoor Khalid,Muhammad Shahzad Sarfraz,Uzair Iqbal,Muhammad Umar Aftab,Gniewko Niedbała,Hafiz Tayyab Rauf +5 more
TL;DR: In this paper , the authors used deep convolutional neural networks (CNNs) for real-time detection of diseases in plant leaves and achieved 93% accuracy on a test set with a random dataset image served as the model input via a cell phone.
References
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Journal ArticleDOI
The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared
Hans-Dieter Seelig,Alexander Hoehn,Louis S. Stodieck,David M. Klaus,William W. Adams,William J. Emery +5 more
TL;DR: In this article, the authors examined the influence of the thickness of leaves upon leaf water indices and found that leaf thickness was correlated with the relative water content (RWC) of leaves, while the R 1300/R 1450 leaf water index also demonstrated a high signal strength and low variability (R 2>0.94).
Journal ArticleDOI
Leaf water dynamics of Arabidopsis thaliana monitored in-vivo using terahertz time-domain spectroscopy
TL;DR: Terahertz spectroscopy is used to study the water dynamics of Arabidopsis thaliana by comparing the dehydration kinetics of leaves from plants under well-irrigated and water deficit conditions and confirms that terahertz can be an excellent non-contact probe of in-vivo tissue hydration.
Journal Article
Introducing terahertz technology into plant biology: A novel method to monitor changes in leaf water status
B. Breitenstein,Maik Scheller,Mohammad Khaled Shakfa,Thomas Kinder,Thomas Müller-Wirts,Martin Koch,Dirk Selmar +6 more
TL;DR: In this article, a novel, non-destructive method for determination of changes in leaf water content in the fi eld based on terahertz (THz) technology was presented.
Estimation of leaf water potential by thermographic and spectral measurements in grapevine
TL;DR: In this paper, the spectral reflectance was used to estimate the leaf water potential (λ) in a Malbec vineyard in Mendoza, Argentina, and two regression models derived from the thermographic data were tested.